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Hey peoples! Tonight, some meta-words. As you know I am fascinated by compilers and language implementations, and I just want to know all the things and implement all the fun stuff: intermediate representations, flow-sensitive source-to-source optimization passes, register allocation, instruction selection, garbage collection, all of that. It started long ago with a combination of curiosity and a hubris to satisfy that curiosity. The usual way to slake such a thirst is structured higher education followed by industry apprenticeship, but for whatever reason my path sent me through a nuclear engineering bachelor’s program instead of computer science, and continuing that path was so distasteful that I noped out all the way to rural Namibia for a couple years. Fast-forward, after 20 years in the programming industry, and having picked up some language implementation experience, a few years ago I returned to garbage collection. I have a good level of language implementation chops but never wrote a memory manager, and Guile’s performance was limited by its use of the Boehm collector. I had been on the lookout for something that could help, and when I learned of it seemed to me that the only thing missing was an appropriate implementation for Guile, and hey I could do that!Immix I started with the idea of an -style interface to a memory manager that was abstract enough to be implemented by a variety of different collection algorithms. This kind of abstraction is important, because in this domain it’s easy to convince oneself that a given algorithm is amazing, just based on vibes; to stay grounded, I find I always need to compare what I am doing to some fixed point of reference. This GC implementation effort grew into , but as it did so a funny thing happened: the as a direct replacement for the Boehm collector maintained mark bits in a side table, which I realized was a suitable substrate for Immix-inspired bump-pointer allocation into holes. I ended up building on that to develop an Immix collector, but without lines: instead each granule of allocation (16 bytes for a 64-bit system) is its own line.MMTkWhippetmark-sweep collector that I prototyped The is funny, because it defines itself as a new class of collector, fundamentally different from the three other fundamental algorithms (mark-sweep, mark-compact, and evacuation). Immix’s are blocks (64kB coarse-grained heap divisions) and lines (128B “fine-grained” divisions); the innovation (for me) is the discipline by which one can potentially defragment a block without a second pass over the heap, while also allowing for bump-pointer allocation. See the papers for the deets!Immix papermark-regionregionsoptimistic evacuation However what, really, are the regions referred to by ? If they are blocks, then the concept is trivial: everyone has a block-structured heap these days. If they are spans of lines, well, how does one choose a line size? As I understand it, Immix’s choice of 128 bytes was to be fine-grained enough to not lose too much space to fragmentation, while also being coarse enough to be eagerly swept during the GC pause.mark-region This constraint was odd, to me; all of the mark-sweep systems I have ever dealt with have had lazy or concurrent sweeping, so the lower bound on the line size to me had little meaning. Indeed, as one reads papers in this domain, it is hard to know the real from the rhetorical; the review process prizes novelty over nuance. Anyway. What if we cranked the precision dial to 16 instead, and had a line per granule? That was the process that led me to Nofl. It is a space in a collector that came from mark-sweep with a side table, but instead uses the side table for bump-pointer allocation. Or you could see it as an Immix whose line size is 16 bytes; it’s certainly easier to explain it that way, and that’s the tack I took in a .recent paper submission to ISMM’25 Wait what! I have a fine job in industry and a blog, why write a paper? Gosh I have meditated on this for a long time and the answers are very silly. Firstly, one of my language communities is Scheme, which was a research hotbed some 20-25 years ago, which means many practitioners—people I would be pleased to call peers—came up through the PhD factories and published many interesting results in academic venues. These are the folks I like to hang out with! This is also what academic conferences are, chances to shoot the shit with far-flung fellows. In Scheme this is fine, my work on Guile is enough to pay the intellectual cover charge, but I need more, and in the field of GC I am not a proven player. So I did an atypical thing, which is to cosplay at being an independent researcher without having first been a dependent researcher, and just solo-submit a paper. Kids: if you see yourself here, just go get a doctorate. It is not easy but I can only think it is a much more direct path to goal. And the result? Well, friends, it is this blog post :) I got the usual assortment of review feedback, from the very sympathetic to the less so, but ultimately people were confused by leading with a comparison to Immix but ending without an evaluation against Immix. This is fair and the paper does not mention that, you know, I don’t have an Immix lying around. To my eyes it was a good paper, an , but, you know, just a try. I’ll try again sometime.80% paper In the meantime, I am driving towards getting Whippet into Guile. I am hoping that sometime next week I will have excised all the uses of the BDW (Boehm GC) API in Guile, which will finally allow for testing Nofl in more than a laboratory environment. Onwards and upwards! whippet regions? paper??!?
Today, some more words on memory management, on the practicalities of a system with conservatively-traced references. The context is that I have finally started banging into , initially in a configuration that continues to use the conservative Boehm-Demers-Weiser (BDW) collector behind the scene. In that way I can incrementally migrate over all of the uses of the BDW API in Guile to use Whippet API instead, and then if all goes well, I should be able to switch Whippet to use another GC algorithm, probably the . MMC scales better than BDW for multithreaded mutators, and it can eliminate fragmentation via Immix-inspired optimistic evacuation.WhippetGuilemostly-marking collector (MMC) A garbage-collected heap consists of memory, which is a set of addressable locations. An object is a disjoint part of a heap, and is the unit of allocation. A field is memory within an object that may refer to another object by address. Objects are nodes in a directed graph in which each edge is a field containing an object reference. A root is an edge into the heap from outside. Garbage collection reclaims memory from objects that are not reachable from the graph that starts from a set of roots. Reclaimed memory is available for new allocations. In the course of its work, a collector may want to relocate an object, moving it to a different part of the heap. The collector can do so if it can update all edges that refer to the object to instead refer to its new location. Usually a collector arranges things so all edges have the same representation, for example an aligned word in memory; updating an edge means replacing the word’s value with the new address. Relocating objects can improve locality and reduce fragmentation, so it is a good technique to have available. (Sometimes we say evacuate, move, or compact instead of relocate; it’s all the same.) Some collectors allow edges: words in memory whose value may be the address of an object, or might just be scalar data. Ambiguous edges usually come about if a compiler doesn’t precisely record which stack locations or registers contain GC-managed objects. Such ambiguous edges must be traced : the collector adds the object to its idea of the set of live objects, as if the edge were a real reference. This tracing mode isn’t supported by all collectors.ambiguousconservatively Any object that might be the target of an ambiguous edge cannot be relocated by the collector; a collector that allows conservative edges cannot rely on relocation as part of its reclamation strategy. Still, if the collector can know that a given object will not be the referent of an ambiguous edge, relocating it is possible. How can one know that an object is not the target of an ambiguous edge? We have to partition the heap somehow into possibly-conservatively-referenced and definitely-not-conservatively-referenced. The two ways that I know to do this are spatially and temporally. Spatial partitioning means that regardless of the set of root and intra-heap edges, there are some objects that will never be conservatively referenced. This might be the case for a type of object that is “internal” to a language implementation; third-party users that may lack the discipline to precisely track roots might not be exposed to objects of a given kind. Still, link-time optimization tends to weather these boundaries, so I don’t see it as being too reliable over time. Temporal partitioning is more robust: if all ambiguous references come from roots, then if one traces roots before intra-heap edges, then any object not referenced after the roots-tracing phase is available for relocation. So let’s talk about Guile! Guile uses BDW currently, which considers edges to be ambiguous by default. However, given that objects carry type tags, Guile can, with relatively little effort, switch to precisely tracing most edges. “Most”, however, is not sufficient; to allow for relocation, we need to intra-heap ambiguous edges, to confine conservative tracing to the roots-tracing phase.eliminate Conservatively tracing references from C stacks or even from static data sections is not a problem: these are roots, so, fine. Guile currently traces Scheme stacks almost-precisely: its compiler emits stack maps for every call site, which uses liveness analysis to only mark those slots that are Scheme values that will be used in the continuation. However it’s possible that any given frame is marked conservatively. The most common case is when using the BDW collector and a thread is pre-empted by a signal; then its most recent stack frame is likely not at a safepoint and indeed is likely undefined in terms of Guile’s VM. It can also happen if there is a call site within a VM operation, for example to a builtin procedure, if it throws an exception and recurses, or causes GC itself. Also, when are enabled, we can run Scheme between any two Guile VM operations.per-instruction traps So, Guile could change to trace Scheme stacks fully precisely, but this is a lot of work; in the short term we will probably just trace Scheme stacks as roots instead of during the main trace. However, there is one more significant source of ambiguous roots, and that is reified continuation objects. Unlike active stacks, these have to be discovered during a trace and cannot be partitioned out to the root phase. For delimited continuations, these consist of a slice of the Scheme stack. Traversing a stack slice precisely is less problematic than for active stacks, because it isn’t in motion, and it is captured at a known point; but we will have to deal with stack frames that are pre-empted in unexpected locations due to exceptions within builtins. If a stack map is missing, probably the solution there is to reconstruct one using local flow analysis over the bytecode of the stack frame’s function; time-consuming, but it should be robust as we do it elsewhere. Undelimited continuations (those captured by ) contain a slice of the C stack also, for historical reasons, and there we can’t trace it precisely at all. Therefore either we disable relocation if there are any live undelimited continuation objects, or we eagerly pin any object referred to by a freshly captured stack slice.call/cc If you want to follow along with the Whippet-in-Guile work, see the branch in Git. I’ve bumped its version to 4.0 because, well, why the hell not; if it works, it will certainly be worth it. Until next time, happy hacking!wip-whippet problem statement: how to manage ambiguous edges kinds of ambiguous edges in guile fin
Earlier this weekGuileWhippet But now I do! Today’s note is about how we can support untagged allocations of a few different kinds in Whippet’s .mostly-marking collector Why bother supporting untagged allocations at all? Well, if I had my way, I wouldn’t; I would just slog through Guile and fix all uses to be tagged. There are only a finite number of use sites and I could get to them all in a month or so. The problem comes for uses of from outside itself, in C extensions and embedding programs. These users are loathe to adapt to any kind of change, and garbage-collection-related changes are the worst. So, somehow, we need to support these users if we are not to break the Guile community.scm_gc_malloclibguile The problem with , though, is that it is missing an expression of intent, notably as regards tagging. You can use it to allocate an object that has a tag and thus can be traced precisely, or you can use it to allocate, well, anything else. I think we will have to add an API for the tagged case and assume that anything that goes through is requesting an untagged, conservatively-scanned block of memory. Similarly for : you could be allocating a tagged object that happens to not contain pointers, or you could be allocating an untagged array of whatever. A new API is needed there too for pointerless untagged allocations.scm_gc_mallocscm_gc_mallocscm_gc_malloc_pointerless Recall that the mostly-marking collector can be built in a number of different ways: it can support conservative and/or precise roots, it can trace the heap precisely or conservatively, it can be generational or not, and the collector can use multiple threads during pauses or not. Consider a basic configuration with precise roots. You can make tagged pointerless allocations just fine: the trace function for that tag is just trivial. You would like to extend the collector with the ability to make pointerless allocations, for raw data. How to do this?untagged Consider first that when the collector goes to trace an object, it can’t use bits inside the object to discriminate between the tagged and untagged cases. Fortunately though . Of those 8 bits, 3 are used for the mark (five different states, allowing for future concurrent tracing), two for the , one to indicate whether the object is pinned or not, and one to indicate the end of the object, so that we can determine object bounds just by scanning the metadata byte array. That leaves 1 bit, and we can use it to indicate untagged pointerless allocations. Hooray!the main space of the mostly-marking collector has one metadata byte for each 16 bytes of payloadprecise field-logging write barrier However there is a wrinkle: when Whippet decides the it should evacuate an object, it tracks the evacuation state in the object itself; the embedder has to provide an implementation of a , allowing the collector to detect whether an object is forwarded or not, to claim an object for forwarding, to commit a forwarding pointer, and so on. We can’t do that for raw data, because all bit states belong to the object, not the collector or the embedder. So, we have to set the “pinned” bit on the object, indicating that these objects can’t move.little state machine We could in theory manage the forwarding state in the metadata byte, but we don’t have the bits to do that currently; maybe some day. For now, untagged pointerless allocations are pinned. You might also want to support untagged allocations that contain pointers to other GC-managed objects. In this case you would want these untagged allocations to be scanned conservatively. We can do this, but if we do, it will pin all objects. Thing is, conservative stack roots is a kind of a sweet spot in language run-time design. You get to avoid constraining your compiler, you avoid a class of bugs related to rooting, but you can still support compaction of the heap. How is this, you ask? Well, consider that you can move any object for which we can precisely enumerate the incoming references. This is trivially the case for precise roots and precise tracing. For conservative roots, we don’t know whether a given edge is really an object reference or not, so we have to conservatively avoid moving those objects. But once you are done tracing conservative edges, any live object that hasn’t yet been traced is fair game for evacuation, because none of its predecessors have yet been visited. But once you add conservatively-traced objects back into the mix, you don’t know when you are done tracing conservative edges; you could always discover another conservatively-traced object later in the trace, so you have to pin everything. The good news, though, is that we have gained an easier migration path. I can now shove Whippet into Guile and get it running even before I have removed untagged allocations. Once I have done so, I will be able to allow for compaction / evacuation; things only get better from here. Also as a side benefit, the mostly-marking collector’s heap-conservative configurations are now faster, because we have metadata attached to objects which allows tracing to skip known-pointerless objects. This regains an optimization that BDW has long had via its , used in Guile since time out of mind.GC_malloc_atomic With support for untagged allocations, I think I am finally ready to start getting Whippet into Guile itself. Happy hacking, and see you on the other side! inside and outside on intent on data on slop fin
Salutations, populations. Today’s note is more of a work-in-progress than usual; I have been finally starting to look at getting into , and there are some open questions.WhippetGuile I started by taking a look at how Guile uses the ‘s API, to make sure I had all my bases covered for an eventual switch to something that was not BDW. I think I have a good overview now, and have divided the parts of BDW-GC used by Guile into seven categories.Boehm-Demers-Weiser collector Firstly there are the ways in which Guile’s run-time and compiler depend on BDW-GC’s behavior, without actually using BDW-GC’s API. By this I mean principally that we assume that any reference to a GC-managed object from any thread’s stack will keep that object alive. The same goes for references originating in global variables, or static data segments more generally. Additionally, we rely on GC objects not to move: references to GC-managed objects in registers or stacks are valid across a GC boundary, even if those references are outside the GC-traced graph: all objects are pinned. Some of these “uses” are internal to Guile’s implementation itself, and thus amenable to being changed, albeit with some effort. However some escape into the wild via Guile’s API, or, as in this case, as implicit behaviors; these are hard to change or evolve, which is why I am putting my hopes on Whippet’s , which allows for conservative roots.mostly-marking collector Then there are the uses of BDW-GC’s API, not to accomplish a task, but to protect the mutator from the collector: , explicitly enabling or disabling GC, calls to that take BDW-GC’s use of POSIX signals into account, and so on. BDW-GC can stop any thread at any time, between any two instructions; for most users is anodyne, but if ever you use weak references, things start to get really gnarly.GC_call_with_alloc_locksigmask Of course a new collector would have its own constraints, but switching to cooperative instead of pre-emptive safepoints would be a welcome relief from this mess. On the other hand, we will require client code to explicitly mark their threads as inactive during calls in more cases, to ensure that all threads can promptly reach safepoints at all times. Swings and roundabouts? Did you know that the Boehm collector allows for precise tracing? It does! It’s slow and truly gnarly, but when you need precision, precise tracing nice to have. (This is the interface.) Guile uses it to mark Scheme stacks, allowing it to avoid treating unboxed locals as roots. When it loads compiled files, Guile also adds some sliced of the mapped files to the root set. These interfaces will need to change a bit in a switch to Whippet but are ultimately internal, so that’s fine.GC_new_kind What is not fine is that Guile allows C users to hook into precise tracing, notably via . This is not only the wrong interface, not allowing for copying collection, but these functions are just truly gnarly. I don’t know know what to do with them yet; are our external users ready to forgo this interface entirely? We have been working on them over time, but I am not sure.scm_smob_set_mark Weak references, weak maps of various kinds: the implementation of these in terms of BDW’s API is incredibly gnarly and ultimately unsatisfying. We will be able to replace all of these with ephemerons and tables of ephemerons, which are natively supported by Whippet. The same goes with finalizers. The same goes for constructs built on top of finalizers, such as ; we’ll get to reimplement these on top of nice Whippet-supplied primitives. Whippet allows for resuscitation of finalized objects, so all is good here.guardians There is a long list of miscellanea: the interfaces to explicitly trigger GC, to get statistics, to control the number of marker threads, to initialize the GC; these will change, but all uses are internal, making it not a terribly big deal. I should mention one API concern, which is that BDW’s state is all implicit. For example, when you go to allocate, you don’t pass the API a handle which you have obtained for your thread, and which might hold some thread-local freelists; BDW will instead load thread-local variables in its API. That’s not as efficient as it could be and Whippet goes the explicit route, so there is some additional plumbing to do. Finally I should mention the true miscellaneous BDW-GC function: . Guile exposes it via an API, . It was already vestigial and we should just remove it, as it has no sensible semantics or implementation.GC_freescm_gc_free That brings me to what I wanted to write about today, but am going to have to finish tomorrow: the actual allocation routines. BDW-GC provides two, essentially: and . The difference is that “atomic” allocations don’t refer to other GC-managed objects, and as such are well-suited to raw data. Otherwise you can think of atomic allocations as a pure optimization, given that BDW-GC mostly traces conservatively anyway.GC_mallocGC_malloc_atomic From the perspective of a user of BDW-GC looking to switch away, there are two broad categories of allocations, tagged and untagged. Tagged objects have attached metadata bits allowing their type to be inspected by the user later on. This is the happy path! We’ll be able to write a function that takes any object, does a switch on, say, some bits in the first word, dispatching to type-specific tracing code. As long as the object is sufficiently initialized by the time the next safepoint comes around, we’re good, and given cooperative safepoints, the compiler should be able to ensure this invariant.gc_trace_object Then there are untagged allocations. Generally speaking, these are of two kinds: temporary and auxiliary. An example of a temporary allocation would be growable storage used by a C run-time routine, perhaps as an unbounded-sized alternative to . Guile uses these a fair amount, as they compose well with non-local control flow as occurring for example in exception handling.alloca An auxiliary allocation on the other hand might be a data structure only referred to by the internals of a tagged object, but which itself never escapes to Scheme, so you never need to inquire about its type; it’s convenient to have the lifetimes of these values managed by the GC, and when desired to have the GC automatically trace their contents. Some of these should just be folded into the allocations of the tagged objects themselves, to avoid pointer-chasing. Others are harder to change, notably for mutable objects. And the trouble is that for external users of , I fear that we won’t be able to migrate them over, as we don’t know whether they are making tagged mallocs or not.scm_gc_malloc One conventional way to handle untagged allocations is to manage to fit your data into other tagged data structures; V8 does this in many places with instances of FixedArray, for example, and Guile should do more of this. Otherwise, you make new tagged data types. In either case, all auxiliary data should be tagged. I think there may be an alternative, which would be just to support the equivalent of untagged and ; but for that, I am out of time today, so type at y’all tomorrow. Happy hacking!GC_mallocGC_malloc_atomic inventory what is to be done? implicit uses defensive uses precise tracing reachability misc allocation
Hey all, quick post today to mention that I added tracing support to the . If the support library for is available when Whippet is compiled, Whippet embedders can visualize the GC process. Like this!Whippet GC libraryLTTng Click above for a full-scale screenshot of the trace explorer processing the with the on a 2.5x heap. Of course no image will have all the information; the nice thing about trace visualizers like is that you can zoom in to sub-microsecond spans to see exactly what is happening, have nice mouseovers and clicky-clickies. Fun times!Perfetto microbenchmarknboyerparallel copying collector Adding tracepoints to a library is not too hard in the end. You need to , which has a file. You need to . Then you have a that includes the header, to generate the code needed to emit tracepoints.pull in the librarylttng-ustdeclare your tracepoints in one of your header filesminimal C filepkg-config Annoyingly, this header file you write needs to be in one of the directories; it can’t be just in the the source directory, because includes it seven times (!!) using (!!!) and because the LTTng file header that does all the computed including isn’t in your directory, GCC won’t find it. It’s pretty ugly. Ugliest part, I would say. But, grit your teeth, because it’s worth it.-Ilttngcomputed includes Finally you pepper your source with tracepoints, which probably you so that you don’t have to require LTTng, and so you can switch to other tracepoint libraries, and so on.wrap in some macro I wrote up a little . It’s not as easy as , which I think is an error. Another ugly point. Buck up, though, you are so close to graphs!guide for Whippet users about how to actually get tracesperf record By which I mean, so close to having to write a Python script to make graphs! Because LTTng writes its logs in so-called Common Trace Format, which as you might guess is not very common. I have a colleague who swears by it, that for him it is the lowest-overhead system, and indeed in my case it has no measurable overhead when trace data is not being collected, but his group uses custom scripts to convert the CTF data that he collects to... (?!?!?!!).GTKWave In my case I wanted to use Perfetto’s UI, so I found a to convert from CTF to the . But, it uses an old version of Babeltrace that wasn’t available on my system, so I had to write a (!!?!?!?!!), probably the most Python I have written in the last 20 years.scriptJSON-based tracing format that Chrome profiling used to usenew script Yes. God I love blinkenlights. As long as it’s low-maintenance going forward, I am satisfied with the tradeoffs. Even the fact that I had to write a script to process the logs isn’t so bad, because it let me get nice nested events, which most stock tracing tools don’t allow you to do. I fixed a small performance bug because of it – a . A win, and one that never would have shown up on a sampling profiler too. I suspect that as I add more tracepoints, more bugs will be found and fixed.worker thread was spinning waiting for a pool to terminate instead of helping out I think the only thing that would be better is if tracepoints were a part of Linux system ABIs – that there would be header files to emit tracepoint metadata in all binaries, that you wouldn’t have to link to any library, and the actual tracing tools would be intermediated by that ABI in such a way that you wouldn’t depend on those tools at build-time or distribution-time. But until then, I will take what I can get. Happy tracing! on adding tracepoints using the thing is it worth it? fin
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Although it looks really good, I have not yet tried the Jujutsu (jj) version control system, mainly because it’s not yet clearly superior to Magit. But I have been following jj discussions with great interest. One of the things that jj has not yet tackled is how to do better than git refs / branches / tags. As I underestand it, jj currently has something like Mercurial bookmarks, which are more like raw git ref plumbing than a high-level porcelain feature. In particular, jj lacks signed or annotated tags, and it doesn’t have branch names that always automatically refer to the tip. This is clearly a temporary state of affairs because jj is still incomplete and under development and these gaps are going to be filled. But the discussions have led me to think about how git’s branches are unsatisfactory, and what could be done to improve them. branch merge rebase squash fork cover letters previous branch workflow questions branch One of the huge improvements in git compared to Subversion was git’s support for merges. Subversion proudly advertised its support for lightweight branches, but a branch is not very useful if you can’t merge it: an un-mergeable branch is not a tool you can use to help with work-in-progress development. The point of this anecdote is to illustrate that rather than trying to make branches better, we should try to make merges better and branches will get better as a consequence. Let’s consider a few common workflows and how git makes them all unsatisfactory in various ways. Skip to cover letters and previous branch below where I eventually get to the point. merge A basic merge workflow is, create a feature branch hack, hack, review, hack, approve merge back to the trunk The main problem is when it comes to the merge, there may be conflicts due to concurrent work on the trunk. Git encourages you to resolve conflicts while creating the merge commit, which tends to bypass the normal review process. Git also gives you an ugly useless canned commit message for merges, that hides what you did to resolve the conflicts. If the feature branch is a linear record of the work then it can be cluttered with commits to address comments from reviewers and to fix mistakes. Some people like an accurate record of the history, but others prefer the repository to contain clean logical changes that will make sense in years to come, keeping the clutter in the code review system. rebase A rebase-oriented workflow deals with the problems of the merge workflow but introduces new problems. Primarily, rebasing is intended to produce a tidy logical commit history. And when a feature branch is rebased onto the trunk before it is merged, a simple fast-forward check makes it trivial to verify that the merge will be clean (whether it uses separate merge commit or directly fast-forwards the trunk). However, it’s hard to compare the state of the feature branch before and after the rebase. The current and previous tips of the branch (amongst other clutter) are recorded in the reflog of the person who did the rebase, but they can’t share their reflog. A force-push erases the previous branch from the server. Git forges sometimes make it possible to compare a branch before and after a rebase, but it’s usually very inconvenient, which makes it hard to see if review comments have been addressed. And a reviewer can’t fetch past versions of the branch from the server to review them locally. You can mitigate these problems by adding commits in --autosquash format, and delay rebasing until just before merge. However that reintroduces the problem of merge conflicts: if the autosquash doesn’t apply cleanly the branch should have another round of review to make sure the conflicts were resolved OK. squash When the trunk consists of a sequence of merge commits, the --first-parent log is very uninformative. A common way to make the history of the trunk more informative, and deal with the problems of cluttered feature branches and poor rebase support, is to squash the feature branch into a single commit on the trunk instead of mergeing. This encourages merge requests to be roughly the size of one commit, which is arguably a good thing. However, it can be uncomfortably confining for larger features, or cause extra busy-work co-ordinating changes across multiple merge requests. And squashed feature branches have the same merge conflict problem as rebase --autosquash. fork Feature branches can’t always be short-lived. In the past I have maintained local hacks that were used in production but were not (not yet?) suitable to submit upstream. I have tried keeping a stack of these local patches on a git branch that gets rebased onto each upstream release. With this setup the problem of reviewing successive versions of a merge request becomes the bigger problem of keeping track of how the stack of patches evolved over longer periods of time. cover letters Cover letters are common in the email patch workflow that predates git, and they are supported by git format-patch. Github and other forges have a webby version of the cover letter: the message that starts off a pull request or merge request. In git, cover letters are second-class citizens: they aren’t stored in the repository. But many of the problems I outlined above have neat solutions if cover letters become first-class citizens, with a Jujutsu twist. A first-class cover letter starts off as a prototype for a merge request, and becomes the eventual merge commit. Instead of unhelpful auto-generated merge commits, you get helpful and informative messages. No extra work is needed since we’re already writing cover letters. Good merge commit messages make good --first-parent logs. The cover letter subject line works as a branch name. No more need to invent filename-compatible branch names! Jujutsu doesn’t make you name branches, giving them random names instead. It shows the subject line of the topmost commit as a reminder of what the branch is for. If there’s an explicit cover letter the subject line will be a better summary of the branch as a whole. I often find the last commit on a branch is some post-feature cleanup, and that kind of commit has a subject line that is never a good summary of its feature branch. As a prototype for the merge commit, the cover letter can contain the resolution of all the merge conflicts in a way that can be shared and reviewed. In Jujutsu, where conflicts are first class, the cover letter commit can contain unresolved conflicts: you don’t have to clean them up when creating the merge, you can leave that job until later. If you can share a prototype of your merge commit, then it becomes possible for your collaborators to review any merge conflicts and how you resolved them. To distinguish a cover letter from a merge commit object, a cover letter object has a “target” header which is a special kind of parent header. A cover letter also has a normal parent commit header that refers to earlier commits in the feature branch. The target is what will become the first parent of the eventual merge commit. previous branch The other ingredient is to add a “previous branch” header, another special kind of parent commit header. The previous branch header refers to an older version of the cover letter and, transitively, an older version of the whole feature branch. Typically the previous branch header will match the last shared version of the branch, i.e. the commit hash of the server’s copy of the feature branch. The previous branch header isn’t changed during normal work on the feature branch. As the branch is revised and rebased, the commit hash of the cover letter will change fairly frequently. These changes are recorded in git’s reflog or jj’s oplog, but not in the “previous branch” chain. You can use the previous branch chain to examine diffs between versions of the feature branch as a whole. If commits have Gerrit-style or jj-style change-IDs then it’s fairly easy to find and compare previous versions of an individual commit. The previous branch header supports interdiff code review, or allows you to retain past iterations of a patch series. workflow Here are some sketchy notes on how these features might work in practice. One way to use cover letters is jj-style, where it’s convenient to edit commits that aren’t at the tip of a branch, and easy to reshuffle commits so that a branch has a deliberate narrative. When you create a new feature branch, it starts off as an empty cover letter with both target and parent pointing at the same commit. Alternatively, you might start a branch ad hoc, and later cap it with a cover letter. If this is a small change and rebase + fast-forward is allowed, you can edit the “cover letter” to contain the whole change. Otherwise, you can hack on the branch any which way. Shuffle the commits that should be part of the merge request so that they occur before the cover letter, and edit the cover letter to summarize the preceding commits. When you first push the branch, there’s (still) no need to give it a name: the server can see that this is (probably) going to be a new merge request because the top commit has a target branch and its change-ID doesn’t match an existing merge request. Also when you push, your client automatically creates a new instance of your cover letter, adding a “previous branch” header to indicate that the old version was shared. The commits on the branch that were pushed are now immutable; rebases and edits affect the new version of the branch. During review there will typically be multiple iterations of the branch to address feedback. The chain of previous branch headers allows reviewers to see how commits were changed to address feedback, interdiff style. The branch can be merged when the target header matches the current trunk and there are no conflicts left to resolve. When the time comes to merge the branch, there are several options: For a merge workflow, the cover letter is used to make a new commit on the trunk, changing the target header into the first parent commit, and dropping the previous branch header. Or, if you like to preserve more history, the previous branch chain can be retained. Or you can drop the cover letter and fast foward the branch on to the trunk. Or you can squash the branch on to the trunk, using the cover letter as the commit message. questions This is a fairly rough idea: I’m sure that some of the details won’t work in practice without a lot of careful work on compatibility and deployability. Do the new commit headers (“target” and “previous branch”) need to be headers? What are the compatibility issues with adding new headers that refer to other commits? How would a server handle a push of an unnamed branch? How could someone else pull a copy of it? How feasible is it to use cover letter subject lines instead of branch names? The previous branch header is doing a similar job to a remote tracking branch. Is there an opportunity to simplify how we keep a local cache of the server state? Despite all that, I think something along these lines could make branches / reviews / reworks / merges less awkward. How you merge should me a matter of your project’s preferred style, without interference from technical limitations that force you to trade off one annoyance against another. There remains a non-technical limitation: I have assumed that contributors are comfortable enough with version control to use a history-editing workflow effectively. I’ve lost all perspective on how hard this is for a newbie to learn; I expect (or hope?) jj makes it much easier than git rebase.
In my post yesterday (“ARM is great, ARM is terrible (and so is RISC-V)), I described my desire to find ARM hardware with AES instructions to support full-disk encryption, and the poor state of the OS ecosystem around the newer ARM boards. I was anticipating buying either a newer ARM SBC or an x86 mini … Continue reading Performant Full-Disk Encryption on a Raspberry Pi, but Foiled by Twisty UARTs →
Debates, at their finest, are about exploring topics together in search for truth. That probably sounds hopelessly idealistic to anyone who've ever perused a comment section on the internet, but ideals are there to remind us of what's possible, to inspire us to reach higher — even if reality falls short. I've been reaching for those debating ideals for thirty years on the internet. I've argued with tens of thousands of people, first on Usenet, then in blog comments, then Twitter, now X, and also LinkedIn — as well as a million other places that have come and gone. It's mostly been about technology, but occasionally about society and morality too. There have been plenty of heated moments during those three decades. It doesn't take much for a debate between strangers on this internet to escalate into something far lower than a "search for truth", and I've often felt willing to settle for just a cordial tone! But for the majority of that time, I never felt like things might escalate beyond the keyboards and into the real world. That was until we had our big blow-up at 37signals back in 2021. I suddenly got to see a different darkness from the most vile corners of the internet. Heard from those who seem to prowl for a mob-sanctioned opportunity to threaten and intimidate those they disagree with. It fundamentally changed me. But I used the experience as a mirror to reflect on the ways my own engagement with the arguments occasionally felt too sharp, too personal. And I've since tried to refocus way more of my efforts on the positive and the productive. I'm by no means perfect, and the internet often tempts the worst in us, but I resist better now than I did then. What I cannot come to terms with, though, is the modern equation of words with violence. The growing sense of permission that if the disagreement runs deep enough, then violence is a justified answer to settle it. That sounds so obvious that we shouldn't need to state it in a civil society, but clearly it is not. Not even in technology. Not even in programming. There are plenty of factions here who've taken to justify their violent fantasies by referring to their ideological opponents as "nazis", "fascists", or "racists". And then follow that up with a call to "punch a nazi" or worse. When you hear something like that often enough, it's easy to grow glib about it. That it's just a saying. They don't mean it. But I'm afraid many of them really do. Which brings us to Charlie Kirk. And the technologists who name drinks at their bar after his mortal wound just hours after his death, to name but one of the many, morbid celebrations of the famous conservative debater's death. It's sickening. Deeply, profoundly sickening. And my first instinct was exactly what such people would delight in happening. To watch the rest of us recoil, then retract, and perhaps even eject. To leave the internet for a while or forever. But I can't do that. We shouldn't do that. Instead, we should double down on the opposite. Continue to show up with our ideals held high while we debate strangers in that noble search for the truth. Where we share our excitement, our enthusiasm, and our love of technology, country, and humanity. I think that's what Charlie Kirk did so well. Continued to show up for the debate. Even on hostile territory. Not because he thought he was ever going to convince everyone, but because he knew he'd always reach some with a good argument, a good insight, or at least a different perspective. You could agree or not. Counter or be quiet. But the earnest exploration of the topics in a live exchange with another human is as fundamental to our civilization as Socrates himself. Don't give up, don't give in. Keep debating.
I’ve long been interested in new and different platforms. I ran Debian on an Alpha back in the late 1990s and was part of the Alpha port team; then I helped bootstrap Debian on amd64. I’ve got somewhere around 8 Raspberry Pi devices in active use right now, and the free NNCPNET Internet email service … Continue reading ARM is great, ARM is terrible (and so is RISC-V) →
In my first interview out of college I was asked the change counter problem: Given a set of coin denominations, find the minimum number of coins required to make change for a given number. IE for USA coinage and 37 cents, the minimum number is four (quarter, dime, 2 pennies). I implemented the simple greedy algorithm and immediately fell into the trap of the question: the greedy algorithm only works for "well-behaved" denominations. If the coin values were [10, 9, 1], then making 37 cents would take 10 coins in the greedy algorithm but only 4 coins optimally (10+9+9+9). The "smart" answer is to use a dynamic programming algorithm, which I didn't know how to do. So I failed the interview. But you only need dynamic programming if you're writing your own algorithm. It's really easy if you throw it into a constraint solver like MiniZinc and call it a day. int: total; array[int] of int: values = [10, 9, 1]; array[index_set(values)] of var 0..: coins; constraint sum (c in index_set(coins)) (coins[c] * values[c]) == total; solve minimize sum(coins); You can try this online here. It'll give you a prompt to put in total and then give you successively-better solutions: coins = [0, 0, 37]; ---------- coins = [0, 1, 28]; ---------- coins = [0, 2, 19]; ---------- coins = [0, 3, 10]; ---------- coins = [0, 4, 1]; ---------- coins = [1, 3, 0]; ---------- Lots of similar interview questions are this kind of mathematical optimization problem, where we have to find the maximum or minimum of a function corresponding to constraints. They're hard in programming languages because programming languages are too low-level. They are also exactly the problems that constraint solvers were designed to solve. Hard leetcode problems are easy constraint problems.1 Here I'm using MiniZinc, but you could just as easily use Z3 or OR-Tools or whatever your favorite generalized solver is. More examples This was a question in a different interview (which I thankfully passed): Given a list of stock prices through the day, find maximum profit you can get by buying one stock and selling one stock later. It's easy to do in O(n^2) time, or if you are clever, you can do it in O(n). Or you could be not clever at all and just write it as a constraint problem: array[int] of int: prices = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5, 8]; var int: buy; var int: sell; var int: profit = prices[sell] - prices[buy]; constraint sell > buy; constraint profit > 0; solve maximize profit; Reminder, link to trying it online here. While working at that job, one interview question we tested out was: Given a list, determine if three numbers in that list can be added or subtracted to give 0? This is a satisfaction problem, not a constraint problem: we don't need the "best answer", any answer will do. We eventually decided against it for being too tricky for the engineers we were targeting. But it's not tricky in a solver; include "globals.mzn"; array[int] of int: numbers = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5, 8]; array[index_set(numbers)] of var {0, -1, 1}: choices; constraint sum(n in index_set(numbers)) (numbers[n] * choices[n]) = 0; constraint count(choices, -1) + count(choices, 1) = 3; solve satisfy; Okay, one last one, a problem I saw last year at Chipy AlgoSIG. Basically they pick some leetcode problems and we all do them. I failed to solve this one: Given an array of integers heights representing the histogram's bar height where the width of each bar is 1, return the area of the largest rectangle in the histogram. The "proper" solution is a tricky thing involving tracking lots of bookkeeping states, which you can completely bypass by expressing it as constraints: array[int] of int: numbers = [2,1,5,6,2,3]; var 1..length(numbers): x; var 1..length(numbers): dx; var 1..: y; constraint x + dx <= length(numbers); constraint forall (i in x..(x+dx)) (y <= numbers[i]); var int: area = (dx+1)*y; solve maximize area; output ["(\(x)->\(x+dx))*\(y) = \(area)"] There's even a way to automatically visualize the solution (using vis_geost_2d), but I didn't feel like figuring it out in time for the newsletter. Is this better? Now if I actually brought these questions to an interview the interviewee could ruin my day by asking "what's the runtime complexity?" Constraint solvers runtimes are unpredictable and almost always than an ideal bespoke algorithm because they are more expressive, in what I refer to as the capability/tractability tradeoff. But even so, they'll do way better than a bad bespoke algorithm, and I'm not experienced enough in handwriting algorithms to consistently beat a solver. The real advantage of solvers, though, is how well they handle new constraints. Take the stock picking problem above. I can write an O(n²) algorithm in a few minutes and the O(n) algorithm if you give me some time to think. Now change the problem to Maximize the profit by buying and selling up to max_sales stocks, but you can only buy or sell one stock at a given time and you can only hold up to max_hold stocks at a time? That's a way harder problem to write even an inefficient algorithm for! While the constraint problem is only a tiny bit more complicated: include "globals.mzn"; int: max_sales = 3; int: max_hold = 2; array[int] of int: prices = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5, 8]; array [1..max_sales] of var int: buy; array [1..max_sales] of var int: sell; array [index_set(prices)] of var 0..max_hold: stocks_held; var int: profit = sum(s in 1..max_sales) (prices[sell[s]] - prices[buy[s]]); constraint forall (s in 1..max_sales) (sell[s] > buy[s]); constraint profit > 0; constraint forall(i in index_set(prices)) (stocks_held[i] = (count(s in 1..max_sales) (buy[s] <= i) - count(s in 1..max_sales) (sell[s] <= i))); constraint alldifferent(buy ++ sell); solve maximize profit; output ["buy at \(buy)\n", "sell at \(sell)\n", "for \(profit)"]; Most constraint solving examples online are puzzles, like Sudoku or "SEND + MORE = MONEY". Solving leetcode problems would be a more interesting demonstration. And you get more interesting opportunities to teach optimizations, like symmetry breaking. Because my dad will email me if I don't explain this: "leetcode" is slang for "tricky algorithmic interview questions that have little-to-no relevance in the actual job you're interviewing for." It's from leetcode.com. ↩